Prediction of bond dissociation enthalpy of antioxidant phenols by support vector machine

Antioxidants play crucial roles in scavenging oxidative damages arising from reactive oxygen species. Bond dissociation enthalpy (BDE) of phenolic O-H bond has well been accepted as an indicator of antioxidant activity since phenols donate the hydrogen atom to the free radicals thereby neutralizing...

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Main Authors: Chanin Nantasenamat, Chartchalerm Isarankura-Na-Ayudhya, Thanakorn Naenna, Virapong Prachayasittikul
Other Authors: Mahidol University
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Published: 2018
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Online Access:https://repository.li.mahidol.ac.th/handle/123456789/19066
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spelling th-mahidol.190662018-07-12T09:52:21Z Prediction of bond dissociation enthalpy of antioxidant phenols by support vector machine Chanin Nantasenamat Chartchalerm Isarankura-Na-Ayudhya Thanakorn Naenna Virapong Prachayasittikul Mahidol University Chemistry Physics and Astronomy Antioxidants play crucial roles in scavenging oxidative damages arising from reactive oxygen species. Bond dissociation enthalpy (BDE) of phenolic O-H bond has well been accepted as an indicator of antioxidant activity since phenols donate the hydrogen atom to the free radicals thereby neutralizing its toxic effect. The BDEs from a data set of 39 antioxidant phenols were modeled using computationally inexpensive quantum chemical descriptors with multiple linear regression (MLR), partial least squares (PLS), and support vector machine (SVM). The molecular descriptors of the phenols were derived from calculations at the following theoretical levels: AM1, HF/3-21g(d), B3LYP/3-21g(d), and B3LYP/6-31g(d). Results indicated that when MLR and PLS were used as the regression methods, B3LYP/3-21g(d) gave the best performance with leave-one-out cross-validated correlation coefficients (r) of 0.917 and 0.921, respectively, while the semiempirical AM1 provided slightly lower r of 0.897 and 0.888, respectively. When SVM was used as the regression method no significant difference in the accuracy was observed for models using B3LYP/3-21g(d) and AM1 as indicated by r of 0.968 and 0.966, respectively. The quantitative structure-property relationship (QSPR) model of BDE discussed in this study offers great potential for the design of novel antioxidant phenols with robust properties. © 2008 Elsevier Inc. All rights reserved. 2018-07-12T02:22:21Z 2018-07-12T02:22:21Z 2008-09-01 Article Journal of Molecular Graphics and Modelling. Vol.27, No.2 (2008), 188-196 10.1016/j.jmgm.2008.04.005 10933263 2-s2.0-52049113437 https://repository.li.mahidol.ac.th/handle/123456789/19066 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=52049113437&origin=inward
institution Mahidol University
building Mahidol University Library
continent Asia
country Thailand
Thailand
content_provider Mahidol University Library
collection Mahidol University Institutional Repository
topic Chemistry
Physics and Astronomy
spellingShingle Chemistry
Physics and Astronomy
Chanin Nantasenamat
Chartchalerm Isarankura-Na-Ayudhya
Thanakorn Naenna
Virapong Prachayasittikul
Prediction of bond dissociation enthalpy of antioxidant phenols by support vector machine
description Antioxidants play crucial roles in scavenging oxidative damages arising from reactive oxygen species. Bond dissociation enthalpy (BDE) of phenolic O-H bond has well been accepted as an indicator of antioxidant activity since phenols donate the hydrogen atom to the free radicals thereby neutralizing its toxic effect. The BDEs from a data set of 39 antioxidant phenols were modeled using computationally inexpensive quantum chemical descriptors with multiple linear regression (MLR), partial least squares (PLS), and support vector machine (SVM). The molecular descriptors of the phenols were derived from calculations at the following theoretical levels: AM1, HF/3-21g(d), B3LYP/3-21g(d), and B3LYP/6-31g(d). Results indicated that when MLR and PLS were used as the regression methods, B3LYP/3-21g(d) gave the best performance with leave-one-out cross-validated correlation coefficients (r) of 0.917 and 0.921, respectively, while the semiempirical AM1 provided slightly lower r of 0.897 and 0.888, respectively. When SVM was used as the regression method no significant difference in the accuracy was observed for models using B3LYP/3-21g(d) and AM1 as indicated by r of 0.968 and 0.966, respectively. The quantitative structure-property relationship (QSPR) model of BDE discussed in this study offers great potential for the design of novel antioxidant phenols with robust properties. © 2008 Elsevier Inc. All rights reserved.
author2 Mahidol University
author_facet Mahidol University
Chanin Nantasenamat
Chartchalerm Isarankura-Na-Ayudhya
Thanakorn Naenna
Virapong Prachayasittikul
format Article
author Chanin Nantasenamat
Chartchalerm Isarankura-Na-Ayudhya
Thanakorn Naenna
Virapong Prachayasittikul
author_sort Chanin Nantasenamat
title Prediction of bond dissociation enthalpy of antioxidant phenols by support vector machine
title_short Prediction of bond dissociation enthalpy of antioxidant phenols by support vector machine
title_full Prediction of bond dissociation enthalpy of antioxidant phenols by support vector machine
title_fullStr Prediction of bond dissociation enthalpy of antioxidant phenols by support vector machine
title_full_unstemmed Prediction of bond dissociation enthalpy of antioxidant phenols by support vector machine
title_sort prediction of bond dissociation enthalpy of antioxidant phenols by support vector machine
publishDate 2018
url https://repository.li.mahidol.ac.th/handle/123456789/19066
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